Neural Style Transfer
CS 319: Computer Vision
Professor: Arman Chopikyan
Students: Yeva Avetisyan, Anush Aghinyan
Neural-Style, also known as Neural-Transfer, enables you to transform an image by combining the content of one image with the artistic style of another.
The process involves three images: an input image, a content image, and a style image. The input image is adjusted to reflect the content of the content-image and the artistic style of the style-image.
Paper Overview
AUA
Wassily Kandinsky: Composition VII
Vincent van Gogh: The Starry Night
Kazimir Malevich: The Knifegrinder
Neural Style Transfer
using PyTorch
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from PIL import Image
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
from torchvision.models import vgg19, VGG19_Weights
import copy
# Checking whether GPU is available or not
if torch.cuda.is_available():
print("GPU is available.")
else:
print("GPU is not available. Using CPU.")
GPU is not available. Using CPU.
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.set_default_device(device)
Helper Classes
- Content Loss
- Style Loss
- Importing the Model
# Content Loss
class ContentLoss(nn.Module):
def __init__(self, target,):
super(ContentLoss, self).__init__()
# we 'detach' the target content from the tree used
# to dynamically compute the gradient: this is a stated value,
# not a variable. Otherwise the forward method of the criterion
# will throw an error.
self.target = target.detach()
def forward(self, input):
self.loss = F.mse_loss(input, self.target)
return input
# Style Loss
def gram_matrix(input):
a, b, c, d = input.size() # a=batch size(=1)
# b=number of feature maps
# (c,d)=dimensions of a f. map (N=c*d)
features = input.view(a * b, c * d) # resize F_XL into \hat F_XL
G = torch.mm(features, features.t()) # compute the gram product
# we 'normalize' the values of the gram matrix
# by dividing by the number of element in each feature maps.
return G.div(a * b * c * d)
# Style Loss
class StyleLoss(nn.Module):
def __init__(self, target_feature):
super(StyleLoss, self).__init__()
self.target = gram_matrix(target_feature).detach()
def forward(self, input):
G = gram_matrix(input)
self.loss = F.mse_loss(G, self.target)
return input
# Importing the Model
cnn = vgg19(weights=VGG19_Weights.DEFAULT).features.eval()
cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406])
cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225])
# create a module to normalize input image so we can easily put it in a
# ``nn.Sequential``
class Normalization(nn.Module):
def __init__(self, mean, std):
super(Normalization, self).__init__()
# .view the mean and std to make them [C x 1 x 1] so that they can
# directly work with image Tensor of shape [B x C x H x W].
# B is batch size. C is number of channels. H is height and W is width.
self.mean = torch.tensor(mean).view(-1, 1, 1)
self.std = torch.tensor(std).view(-1, 1, 1)
def forward(self, img):
# normalize ``img``
return (img - self.mean) / self.std
Loading the Images
- Input Image = Content Image
- Sequential Module
- Gradient Descent
- A function that performs the neural transfer
- Running the Algorithm
# Loading the Images
# desired size of the output image
imsize = 512 if torch.cuda.is_available() else 128 # use small size if no GPU
# desired size of the output image
desired_width = 512 # specify your desired width
loader = transforms.Compose([
transforms.Resize((desired_width, desired_width)), # set both dimensions to maintain aspect ratio
transforms.CenterCrop((desired_width, desired_width)), # center crop to the desired size
transforms.ToTensor()])
def image_loader(image_name):
image = Image.open(image_name)
# apply the loader transformations
image = loader(image).unsqueeze(0)
return image.to(device, torch.float)
style_img1 = image_loader("C:/Users/User/Desktop/NST-PyTorch/images/kandinsky1.jpg")
content_img = image_loader("C:/Users/User/Desktop/NST-PyTorch/images/aua.jpg")
assert style_img.size() == content_img.size(), \
"we need to import style and content images of the same size"
# Loading the Images
unloader = transforms.ToPILImage() # reconvert into PIL image
plt.ion()
def imshow(tensor, title=None):
image = tensor.cpu().clone() # we clone the tensor to not do changes on it
image = image.squeeze(0) # remove the fake batch dimension
image = unloader(image)
plt.imshow(image)
if title is not None:
plt.title(title)
plt.pause(0.001) # pause a bit so that plots are updated
plt.figure()
imshow(style_img1, title='Style Image')
plt.figure()
imshow(content_img, title='Content Image')
# Sequential module that has content loss and style loss modules correctly inserted.
# desired depth layers to compute style/content losses :
content_layers_default = ['conv_4']
style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
def get_style_model_and_losses(cnn, normalization_mean, normalization_std,
style_img1, content_img,
content_layers=content_layers_default,
style_layers=style_layers_default):
# normalization module
normalization = Normalization(normalization_mean, normalization_std)
# just in order to have an iterable access to or list of content/style
# losses
content_losses = []
style_losses = []
# assuming that ``cnn`` is a ``nn.Sequential``, so we make a new ``nn.Sequential``
# to put in modules that are supposed to be activated sequentially
model = nn.Sequential(normalization)
i = 0 # increment every time we see a conv
for layer in cnn.children():
if isinstance(layer, nn.Conv2d):
i += 1
name = 'conv_{}'.format(i)
elif isinstance(layer, nn.ReLU):
name = 'relu_{}'.format(i)
# The in-place version doesn't play very nicely with the ``ContentLoss``
# and ``StyleLoss`` we insert below. So we replace with out-of-place
# ones here.
layer = nn.ReLU(inplace=False)
elif isinstance(layer, nn.MaxPool2d):
name = 'pool_{}'.format(i)
elif isinstance(layer, nn.BatchNorm2d):
name = 'bn_{}'.format(i)
else:
raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
model.add_module(name, layer)
if name in content_layers:
# add content loss:
target = model(content_img).detach()
content_loss = ContentLoss(target)
model.add_module("content_loss_{}".format(i), content_loss)
content_losses.append(content_loss)
if name in style_layers:
# add style loss:
target_feature = model(style_img1).detach()
style_loss = StyleLoss(target_feature)
model.add_module("style_loss_{}".format(i), style_loss)
style_losses.append(style_loss)
# now we trim off the layers after the last content and style losses
for i in range(len(model) - 1, -1, -1):
if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):
break
model = model[:(i + 1)]
return model, style_losses, content_losses
# Gradient Descent
def get_input_optimizer(input_img):
# this line to show that input is a parameter that requires a gradient
optimizer = optim.LBFGS([input_img])
return optimizer
# A function that performs the neural transfer.
def run_style_transfer(cnn, normalization_mean, normalization_std,
content_img, style_img1, input_img, num_steps=300,
style_weight=1000000, content_weight=1):
"""Run the style transfer."""
print('Building the style transfer model..')
model, style_losses, content_losses = get_style_model_and_losses(cnn,
normalization_mean, normalization_std, style_img1, content_img)
# We want to optimize the input and not the model parameters so we
# update all the requires_grad fields accordingly
input_img.requires_grad_(True)
# We also put the model in evaluation mode, so that specific layers
# such as dropout or batch normalization layers behave correctly.
model.eval()
model.requires_grad_(False)
optimizer = get_input_optimizer(input_img)
print('Optimizing..')
run = [0]
while run[0] <= num_steps:
def closure():
# correct the values of updated input image
with torch.no_grad():
input_img.clamp_(0, 1)
optimizer.zero_grad()
model(input_img)
style_score = 0
content_score = 0
for sl in style_losses:
style_score += sl.loss
for cl in content_losses:
content_score += cl.loss
style_score *= style_weight
content_score *= content_weight
loss = style_score + content_score
loss.backward()
run[0] += 1
if run[0] % 50 == 0:
print("run {}:".format(run))
print('Style Loss : {:4f} Content Loss: {:4f}'.format(
style_score.item(), content_score.item()))
print()
return style_score + content_score
optimizer.step(closure)
# a last correction...
with torch.no_grad():
input_img.clamp_(0, 1)
return input_img
# Running the Algorithm
output = run_style_transfer(cnn, cnn_normalization_mean, cnn_normalization_std,
content_img, style_img1, input_img)
plt.figure()
imshow(output, title='Output Image')
# sphinx_gallery_thumbnail_number = 4
plt.ioff()
plt.show()
Building the style transfer model.. Optimizing.. run [50]: Style Loss : 123.376175 Content Loss: 31.761866 run [100]: Style Loss : 31.299528 Content Loss: 31.792133 run [150]: Style Loss : 12.050381 Content Loss: 29.977434 run [200]: Style Loss : 6.107763 Content Loss: 27.943752 run [250]: Style Loss : 4.081493 Content Loss: 26.072880 run [300]: Style Loss : 3.111447 Content Loss: 24.817232
AUA + Composition VII
AUA + The Starry Night
AUA + The Knifegrinder
Thank you for your attention!